Secure hashing of dynamic hand signatures using wavelet-fourier compression with BioPhasor mixing and 2N discretization

Yip Wai Kuan, Beng Jin Teoh, David C.L. Ngo

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and 2N discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific 2N discretization acts both as an error correction step as well as a real-to-binary space converter.We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of users hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of 0% and 9.4% for random and skilled forgeries for stolen token (worst case) scenario, and 0% for both forgeries in the genuine token (optimal) scenario.

Original languageEnglish
Article number59125
JournalEurasip Journal on Advances in Signal Processing
Volume2007
DOIs
Publication statusPublished - 2007 Jan 19

Fingerprint

Biometrics
Discrete wavelet transforms
Error correction
Discrete Fourier transforms
Recovery

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

@article{e155805315e54035a9cfbf073cc73985,
title = "Secure hashing of dynamic hand signatures using wavelet-fourier compression with BioPhasor mixing and 2N discretization",
abstract = "We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and 2N discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific 2N discretization acts both as an error correction step as well as a real-to-binary space converter.We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of users hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of 0{\%} and 9.4{\%} for random and skilled forgeries for stolen token (worst case) scenario, and 0{\%} for both forgeries in the genuine token (optimal) scenario.",
author = "Kuan, {Yip Wai} and Teoh, {Beng Jin} and Ngo, {David C.L.}",
year = "2007",
month = "1",
day = "19",
doi = "10.1155/2007/59125",
language = "English",
volume = "2007",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer Publishing Company",

}

TY - JOUR

T1 - Secure hashing of dynamic hand signatures using wavelet-fourier compression with BioPhasor mixing and 2N discretization

AU - Kuan, Yip Wai

AU - Teoh, Beng Jin

AU - Ngo, David C.L.

PY - 2007/1/19

Y1 - 2007/1/19

N2 - We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and 2N discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific 2N discretization acts both as an error correction step as well as a real-to-binary space converter.We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of users hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of 0% and 9.4% for random and skilled forgeries for stolen token (worst case) scenario, and 0% for both forgeries in the genuine token (optimal) scenario.

AB - We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and 2N discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific 2N discretization acts both as an error correction step as well as a real-to-binary space converter.We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of users hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of 0% and 9.4% for random and skilled forgeries for stolen token (worst case) scenario, and 0% for both forgeries in the genuine token (optimal) scenario.

UR - http://www.scopus.com/inward/record.url?scp=33846224825&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33846224825&partnerID=8YFLogxK

U2 - 10.1155/2007/59125

DO - 10.1155/2007/59125

M3 - Article

VL - 2007

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

M1 - 59125

ER -